講演抄録/キーワード |
講演名 |
2020-06-04 14:00
An experimental comparison of CNN- and CRNN-CTC for automatic phrase speech recognition systems using a children's speech database ○Yunzhe Wang・Yu Tian(Hokkaido Univ.)・Yoshikazu Miyanaga(CIST)・Hiroshi Tsutsui(Hokkaido Univ.) SIS2020-9 |
抄録 |
(和) |
(まだ登録されていません) |
(英) |
Children's speech recognition is still a challenging issue. In the case of children's speeches, the accuracy of conventional phrase speech recognition approaches is significantly low. This is mainly owing to the high variability of pronunciation patterns due to children's physical activity. Motivated by this, in this paper, we present a phrase speech recognition system using neural networks. We use a convolutional neural network (CNNs) and its recurrent neural network (RNN) version, say CRNN. Also, both approaches utilize a connectionist temporal classification (CTC) loss function, which allows networks to be trained without any prior alignment. Through experiments using a children's speech database, we show the comparison results of CNN- and CRNN-CTC approaches. |
キーワード |
(和) |
/ / / / / / / |
(英) |
Children's speech recognition / convolutional recurrent neural network (CRNN) / connectionist temporal classification (CTC) / / / / / |
文献情報 |
信学技報, vol. 120, no. 51, SIS2020-9, pp. 49-54, 2020年6月. |
資料番号 |
SIS2020-9 |
発行日 |
2020-05-27 (SIS) |
ISSN |
Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
PDFダウンロード |
SIS2020-9 |
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